class_docstr: Class for performing sampling using simple
  Hamiltonian Monte Carlo
  
  Parameters:
  -----------
  model: Model from which sampling has to be done
  
  simulate_dynamics: A class to propose future values of
    momentum and position in time by simulating
    Hamiltonian Dynamics
  
  References
  ----------
  R.Neal. Handbook of Markov Chain Monte Carlo,
  chapter 5: MCMC Using Hamiltonian Dynamics.
  CRC Press, 2011.
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docstr: Temporary method to fix issue in numpy 0.12 #852
docstr_tok: ["Temporary","method","to","fix","issue","in","numpy","0.12","#","852"]
code: def _get_condition(self, acceptance_prob, a):
      """
      Temporary method to fix issue in numpy 0.12 #852
      """
      # return (1/(acceptance_prob ** a)) > np.power(2.0, -a)
      # return (acceptance_prob ** a) > np.power(2.0, -a)
      return (acceptance_prob * a) > np.log(np.power(2.0, -a))
  
file_path: dragonfly/dragonfly/sampling/hmc.py
imports: from __future__ import division
  from math import sqrt
  import numpy
  from .check_functions import _check_1d_array_object
  from .base import LeapFrog, BaseSimulateHamiltonianDynamics
label_desc: Use image node_id 3 for calling the HamiltonianMC obj's underlying member method code with example usage: obj._get_condition(acceptance_prob, a) and returns: unknown